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the broader area of Machine Learning, including Natural Language Processing and Web & Information Retrieval. According to CSRankings , the section has consistently ranked among the top research environments in
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across Duke and other institutions. Applicants should hold, or expect to complete before the start date, a PhD in computer science, machine learning, natural language processing, or a closely related field
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new theoretical approaches for understanding stability, generalization, and feature learning in large-scale neural networks. Further details and application instructions are available at: https
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modelling. Experience with machine learning, prediction modelling, causal inference, or the integration of multiple data sources is an advantage. Excellent oral and written communication skills in English
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This PhD aims to develop novel robotic monitoring and data processing methods to document changes in dynamic seafloor environments over large spatial scales. Motivation: Understanding how
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This PhD aims to develop novel robotic monitoring and data processing methods to document changes in dynamic seafloor environments over large spatial scales. Motivation: Understanding how
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for probabilistic unsupervised learning for structured biological data. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/307053/3-years-phd-position-in-probabilistic-machine
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quantitative field. Good scientific programming skills, particularly in Python, are required. Experience with atmospheric dynamics, numerical modelling, machine learning, or large meteorological datasets would
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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-temporal-resolution bulk measurements (XAS, Raman spectroscopy, XRD), with particular emphasis on pair distribution function (PDF) analysis. Machine learning approaches will be used to support these analyses